Can artificial intelligence help companies curb carbon emissions?

Purpose The purpose of this paper is to examine the impact of artificial intelligence (AI) on firm carbon emissions. Further, we attempt to estimate the presence of a nonlinear relationship between AI and corporate carbon emissions across industries. In addition, our paper aims to understand the role that green innovation plays in the reduction of carbon emissions by firms. Design/methodology/approach Our research takes advantage of detailed data on enterprise carbon emissions from 2013 to 2022 in China. We assessed the level of AI information in the annual report of companies through a textual analysis of AI keywords. Using a panel OLS regression, we investigate the effect of enterprise adoption of AI on carbon emissions. Findings In an examination of 2,137 Chinese public listed companies between 2013 and 2022, we found a significant relationship between AI technology and corporate carbon emissions. More importantly, we find that this relationship is nonlinear (inverted U-shape). Furthermore, we find that green innovations play a partial mediating role in this process. Finally, we show that firms in labor-intensive industries, with heavy pollution, and in the eastern region show a more prominent inverted U-shaped relationship. Originality/value Our paper fills a literature gap by examining the nonlinear relationship between AI and corporate carbon emissions across industries. In addition, we are interested in understanding the role that green innovation plays in firms' efforts to reduce carbon emissions. Findings in our paper could have important policy and practice implications for academics, policymakers and investors.

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Publication Details

Journal
China Finance Review International
Published
2026-02-05
DOI
https://doi.org/10.1108/cfri-03-2025-0172
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
0.00
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article

Can artificial intelligence help companies curb carbon emissions?

Dan Wang, Feng Zhan
China Finance Review International
Energy, Environment, Economic Growth
article

Can artificial intelligence help companies curb carbon emissions?

Dan Wang, Feng Zhan
article en

Abstract

Purpose The purpose of this paper is to examine the impact of artificial intelligence (AI) on firm carbon emissions. Further, we attempt to estimate the presence of a nonlinear relationship between AI and corporate carbon emissions across industries. In addition, our paper aims to understand the role that green innovation plays in the reduction of carbon emissions by firms. Design/methodology/approach Our research takes advantage of detailed data on enterprise carbon emissions from 2013 to 2022 in China. We assessed the level of AI information in the annual report of companies through a textual analysis of AI keywords. Using a panel OLS regression, we investigate the effect of enterprise adoption of AI on carbon emissions. Findings In an examination of 2,137 Chinese public listed companies between 2013 and 2022, we found a significant relationship between AI technology and corporate carbon emissions. More importantly, we find that this relationship is nonlinear (inverted U-shape). Furthermore, we find that green innovations play a partial mediating role in this process. Finally, we show that firms in labor-intensive industries, with heavy pollution, and in the eastern region show a more prominent inverted U-shaped relationship. Originality/value Our paper fills a literature gap by examining the nonlinear relationship between AI and corporate carbon emissions across industries. In addition, we are interested in understanding the role that green innovation plays in firms' efforts to reduce carbon emissions. Findings in our paper could have important policy and practice implications for academics, policymakers and investors.

China Finance Review International
Western University (CA), Shanghai University of Engineering Science (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 69%
Energy, Environment, Economic Growth
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